Cyber Security Radar

Explore our Cyber Security Radar to learn more about identified cyber security trends and the projects we initiate by clicking the numbered circles. Light blue trends include projects.

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AIwareness

Started in August 2021

Targeted phishing on employees is a persistent and concerning threat. Accurate detection of targeted phishing on employees is therefore a very valuable challenge to solve, since it enables organizations to stop adversaries early in the kill-chain.

According to the PCSI Cyber Security Radar, spear phishing was the most used attack for intial access by APT’s in 2010, and ever still in 2020. Over the past 2 years the PCSI partners have developed several machine-learning technologies for detecting targeted phishing on employees. The main challenge in this research was to validate the performance of such detection technologies. The main reason: it is time consuming to manually confirm whether an email is malicious, and in this process you will usually have to involve both the target employee and a security expert.

The employee itself is most knowledgeable about whether an email is contextually ‘normal’, a computer can best detect technological abnormalities, and the security analyst is the right person for drawing a final conclusion on whether an email is malicious or benign.

Project proposal

In this project we propose to develop a system in which the employee (the target), SOC analyst, and detection capabilities all come together in a positive feedback loop. It will support the employee to be resilient for phishing at the right time, it will decrease the workload for SOC analysts, and lastly both the employee’s and SOC analyst’s feedback will help improve the detection algorithm performance. We believe this idea is unique in the sense that it propose to utilize machine learning based detection technologies, employee contextual knowledge, and a SOC analysts expertise in synergy.

Our aims at the end of the project:
•    Improved employee resilience against phishing, supported by machine learning based detection capabilities that can alert a user at the right time
•    Continuous reduction of workload for SOC analysts when handling phishing alerts
•    Continuous improvement of detection algorithms, utilizing all incoming feedback from employees and SOC analysts

Why do we want to work on this idea within the PCSI?

Since targeted phishing on employees is a persistent and hard-to-solve challenge we believe that cooperation between the PCSI partners can bring us one step further. Another conclusion that can be drawn from the fact that the most popular method for initial access by APT’s over the past 10 years has been, and still is, spear phishing, is that clearly no market product has fully solved this challenge.

Conclusions at the end of the Explore phase

In the first phase of this project we have explored whether the proposed solution is of added value for the PCSI partners, next to the anti-phishing solutions currently in use. Multiple PCSI organizations are interested in the outcomes of this research, and at least one partner is willing to explore implementation of a prototype.

In order to develop the proposed solution we plan on utilizing three concepts in one combined solution. These concepts are:

  • Active learning: a machine learning technique that can help to efficiently label data, and therefore efficiently improve an existing model 
  • Explainable AI: explain how a machine learning model assess a certain observation. In our case this would mean that we can explain why a model believes a certain email is malicious or benign 
  • Empowering the user: a research topic focusing on presenting technical information understandably to a non-technical user

Active learning needs an oracle (a human expert) to label data, which in our use-case would be the employee which has most contextual knowledge about her/his email inbox. However, an employee generally lacks the right amount of technical cyber security knowledge, and thus we will feed the employee with information from the technical assessment performed by a computer, for which we will use explainable AI. Generally speaking explainable AI will still result in information that is technical in nature. Therefore we need the ideas from research on presenting technical information understandably in order to empower the employee.

Activities in the PoC phase

In the next phase of this project we will focus on technical feasibility. We will take an existing detection tool, CERTITUDE, which was partly developed in a predecessor of the PCSI (the SRP), and extend it with both active learning and explainable AI functionality. Furthermore, we will develop a design that enables us to present technical output from CERTITUDE understandably to (non-technical) employees. Lastly, we will explore the possibilities of implementation of a pilot at one of the involved PCSI organizations.

This project is part of the trend

3 Threat April 2022

Evolvement of highly personalized social engineering

Social engineering is a technique of manipulating people so they give up confidential information. People are being tricked by personalized content, phone calls and scams. Phishing is a type of common social engineering scam that attempts to fraudulently obtain sensitive information using email. By using spear phising a single individual will be targeted with a more personal approach. For example deepfake voice technology allows people to spoof the voices of other people and commit identity fraud.